Senior Scientist, Translational Computational Biology
About The Position
The Informatics and Predictive Sciences (IPS) mission is to Pioneer, Partner and Predict to drive transformative insights for patient benefit. IPS conducts applied computational research in areas that include genomic, structural and molecular informatics, computational and systems biology, patient selection and translational biomarker research, and broader fields including knowledge science, epidemiology and machine learning—across the full lifecycle of drug discovery and development and across all therapeutic areas at BMS. We do this in close partnership with scientific and clinical experts in the field, both inside and outside the company. We perform innovative science to empower key data-driven decisions across a rich pipeline of next-generation medicines. In doing so, our work transforms the lives of patients, as well as our own lives and careers. Here, you’ll get the chance to grow and thrive through opportunities that are uncommon in scale and scope. You’ll pursue innovative ideas while advancing professionally alongside some of the brightest minds in biopharma. Position Summary The Oncology Translational IPS team is seeking a Senior Scientist, Translational Computational Biology, to serve as the computational partner to our oncology drug development programs across discovery, translational research, and early clinical development. You will translate patient-derived molecular, spatial, clinical, and real-world data into biomarker hypotheses, patient stratification strategies, indication prioritization, pharmacodynamic readouts, and decision-grade recommendations. The majority of the role is embedded with oncology drug development programs and clinical development teams. The remainder builds computational capability for the broader portfolio: AI-enabled translational science, spatial biology, and reusable analytical methods. The exact emphasis of that capability work will evolve with portfolio priorities and emerging technologies. This role is for someone who understands drug development, not only data analysis. We are looking for a scientist with a working understanding of the path from target validation and candidate selection through IND-enabling work and early clinical studies (including dose escalation and expansion), and of the strategic role biomarkers play at each stage, who can carry an interpretation into the forum where the decision is actually made. What you will have to work with: Clinical and multi-modal patient-derived datasets from BMS's industry-leading early-stage clinical studies in oncology: the molecular and clinical biomarker data generated by our own early-phase trials, spanning RNA-seq, WES, TCR-seq, ctDNA and CTC, together with flow cytometry, cytokine profiling, IHC, and proteomics. Layered on top of that: spatial transcriptomics and multiplex immunofluorescence across multiple concurrent oncology programs, backed by a pan-cancer spatial atlas license and an H&E-to-mIF platform partnership; linked genomic-clinical real-world data at scale; and cloud compute alongside a translational informatics team that builds its own methods. These platforms are already funded; this role exists to realize their scientific value. Your contributions will influence development strategies and play a vital role in propelling the BMS early-stage oncology pipeline forward, directly impacting the treatment of cancer patients. You will apply these data across two areas: Oncology drug development program, translational, and early clinical development support. The majority of the role. Biomarker strategy; patient selection and stratification; indication prioritization; target validation; IND-enabling and early clinical trial interpretation; data-driven recommendations for program decisions. Computational innovation and portfolio capability. The remainder. AI-enabled translational science; spatial biology; multimodal integration; reusable workflows, automation, and scalable analytical methods that serve the portfolio rather than a single program.
Requirements
- Bachelor's Degree 7+ years of academic / industry experience
- Master's Degree 5+ years of academic / industry experience
- PhD 2+ years of academic / industry experience
Nice To Haves
- Ph.D. in computational biology, bioinformatics, biostatistics, statistics, human genetics, computer science, or a related quantitative field, with 2+ years of relevant academic and/or industry experience.
- Demonstrated experience analyzing, integrating, and interpreting high-dimensional patient-derived molecular data in oncology or another translational disease area.
- Strong programming skills in R and/or Python, with practical experience in reproducible analysis and data visualization.
- Working knowledge of the oncology drug development process, sufficient to anticipate what a program needs at target validation, candidate selection, IND-enabling work, and early clinical development.
- Clear scientific communication and the ability to collaborate effectively with biology, translational medicine, clinical development, statistics, and quantitative science partners.
- Prior experience in oncology drug development at a biopharmaceutical company, in translational sciences, discovery, or early clinical development.
- AI and machine learning applied to translational problems: LLM-based evidence and literature extraction, agentic or multi-step analytical workflows, biological foundation models, or multimodal representation learning.
- Experience with biomarker strategy, patient selection, pharmacodynamic readouts, companion diagnostic (CDx) development, or clinical translational data interpretation.
- Spatial biology: hands-on experience with spatial transcriptomics (e.g., Visium, Xenium, CosMx, GeoMx, MERFISH) and/or spatial proteomics and multiplex immunofluorescence (e.g., Lunaphore COMET, RareCyte Orion, Akoya), including cell segmentation, phenotyping, and neighborhood or spatial statistics; familiarity with the analysis stack (Squidpy, SpatialData, scverse) and with digital pathology tooling (HALO, QuPath).
- Causal and driver inference, regulatory network analysis, or other approaches that nominate and prioritize targets from patient molecular data.
- Perturbation biology and functional genomics: CRISPR screens, Perturb-seq, and genetic validation in patient-derived model systems, including integrating perturbation readouts against patient data.
- Cell-type inference and gene-expression deconvolution from bulk, single-cell, and spatial data.
- Large-scale real-world oncology data linking genomic, clinical, and EHR-derived information, and familiarity with the statistical issues these data carry (confounding, missingness, cohort selection, longitudinal follow-up).
- Reproducible engineering practice: workflow managers (e.g., Nextflow, Snakemake), version control (Git), high-performance computing, and cloud platforms.
- A record of methods development evidenced by peer-reviewed publications and, ideally, released open-source tools or packages.
- A collaborative problem-solver who can operate in ambiguous program settings and translate complex computational output into practical recommendations.
Responsibilities
- Serve as the translational computational scientist for assigned oncology drug development programs across the discovery-to-early-clinical continuum, from target validation through early clinical studies.
- Shape biomarker strategy, patient selection and stratification hypotheses, pharmacodynamic marker plans, indication prioritization, and enrichment approaches.
- Analyze and integrate multimodal molecular, clinical, and translational datasets from oncology studies, including bulk and single-cell RNA-seq, WES, ctDNA and liquid biopsy, TCR-seq, flow cytometry, cytokine profiling, IHC, proteomics, and spatial readouts.
- Use patient molecular data, causal and driver inference, regulatory network analysis, perturbation readouts, and orthogonal evidence to support target nomination, validation, candidate selection, and IND-enabling decisions.
- Translate complex multimodal analyses into clear, decision-grade biological narratives. Every result ships with an interpretation, its limitations, and a recommendation, and you carry that recommendation to the program team, translational review, or governance forum where the decision is made.
- Design and deploy AI approaches for evidence integration and hypothesis generation across patient omics, genetic evidence, perturbation data, and the literature, including LLM-based extraction, agentic and multi-step workflows, and emerging biological foundation models. Given strategic direction, you will have the autonomy to scope, build, and deploy the methods that become the team's translational decision infrastructure. You should not just run existing tools; we want someone who sees what is missing from current approaches and builds it.
- Take dedicated analytical ownership of spatial data across the portfolio (spatial transcriptomics and spatial proteomics / multiplex immunofluorescence), and realize the scientific value of the atlas, platform, and vendor investments already committed. You will partner with digital pathology and image-analysis colleagues on H&E whole-slide analysis; deep prior digital pathology experience is welcome but not required.
- Apply statistical inference and machine learning to linked genomic-clinical real-world data to support cohort definition and patient stratification, extending the team's existing real-world capability in partnership with our real-world data and epidemiology colleagues.
- Build reproducible workflows and cloud-ready pipelines for multimodal data (single-cell, CRISPR and Perturb-seq screens, spatial), so capability persists as a team asset rather than as one-off analyses.
- Mentor junior scientists and interns, document methods to publication-quality standards, and help raise the computational maturity of the broader translational organization.
Benefits
- Medical, pharmacy, dental, and vision care.
- BMS Well-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
- 401(k) plan, short- and long-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
- Paid Time Off
- flexible time off (unlimited, with manager approval, 11 paid national holidays)
- 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays
- unlimited paid sick time
- up to 2 paid volunteer days per year
- summer hours flexibility
- leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs
- an annual Global Shutdown between Christmas and New Years Day.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
